The incidence and severity of C. difficile infection (CDI) are increasing at an alarming rate. Current efforts to prevent CDI in the healthcare setting focus on preventing the transmission of C. difficile from patients with CDI. The quality of data to support this approach in general is weak, and prevention efforts are hampered by conflicting data. Innovative approaches to preventing CDI are needed, specifically more data are necessary to identify patients at highest risk for CDI and to assess the potential benefits of "reverse" contact precautions, prophylactic oral vancomycin, and education to improve medication prescribing interventions in order to design a cost-effective CDI prevention trial. We developed a CDI risk prediction index utilizing data available real-time from our hospital informatics database, including data on severity of illness, antimicrobial exposures, and exposure to patients with CDI. We propose to validate this CDI risk prediction index and model the cost-effectiveness of different interventions ("reverse" contact precautions, prophylactic oral vancomycin, and education to improve medication prescribing) to prevent CDI in patients at highest risk for CDI. The Specific Aims of this research proposal are: 1) to validate a CDI risk prediction index that automatically calculates each patient's CDI risk each day the patient is in the hospital, and 2) to model the risk and benefits of different interventions to prevent CDI focusing on patients identified as being high risk for CDI by the CDI risk prediction index. PUBLIC HEALTH RELEVANCE: CDI is a significant healthcare-associated, antibiotic resistant pathogen of increasing importance in the US and abroad. The proposed research will contribute to our knowledge of potential novel methods to prevent CDI.